>> echo timestamp=2021-10-07T00:38:00Z a=160.2 b=20.8 c=1 | matplot

matplot.py

#!/usr/bin/env python3
# type: ignore

# The MIT License (MIT) 
# Copyright (c) 2022-present Nathan Todd-Stone 
# https://en.wikipedia.org/wiki/MIT_License#License_terms

import matplotlib as mpl
from matplotlib import pyplot # pip install matplotlib
import argh # pip install argh
import pandas # pip install pandas
import PyQt5 # pip install pyqt5

import sys
import datetime
import os

colors = {
    "base03":  "#002B36",
    "base02":  "#073642",
    "base01":  "#586e75",
    "base00":  "#657b83",
    "base0":   "#839496",
    "base1":   "#93a1a1",
    "base2":   "#EEE8D5",
    "base3":   "#FDF6E3",
    "yellow":  "#B58900",
    "orange":  "#CB4B16",
    "red":     "#DC322F",
    "magenta": "#D33682",
    "violet":  "#6C71C4",
    "blue":    "#268BD2",
    "cyan":    "#2AA198",
    "green":   "#859900",
}

line_color = [
    colors[x] for x in [
        "base00",
        "base3",
        "yellow",
        "red",
        "violet",
        "cyan",
        "green",
    ]
]

line_style = [
    s
    for s in [
        '-',
        '--',
        '-.',
        ':',
        '.',
        ',',
        'o',
        'v',
        '^',
        '<',
        '>',
        '1',
        '2',
        '3',
        '4',
        's',
        'p',
        '*',
        'h',
        'H',
        '+',
        'x',
        'D',
        'd',
        '|',
        '_',
    ]
    for _ in line_color
]

def solarized():
    params = {"ytick.color": colors["base0"],
              "xtick.color": colors["base0"],
              "text.color": colors["base0"],
              "savefig.facecolor": colors["base03"],
              "patch.facecolor": colors["blue"],
              "patch.edgecolor": colors["base0"],
              "grid.color": colors["base0"],
              "figure.edgecolor": colors["base03"],
              "figure.facecolor": colors["base02"],
              "axes.edgecolor": colors["base0"],
              "axes.facecolor": colors["base03"],
              "axes.labelcolor": colors["base00"]}
    mpl.rcParams.update(params)

def to_num(x):
    try:
        k, v = x.split('=')
    except:
        print("fatal:", x)
        sys.exit(1)
    if '.' in v and v.replace('.', '').replace('e+', '').replace('e-', '').replace('-', '').isdigit():
        return [k, float(v)]
    elif v.replace('-', '').isdigit():
        return [k, int(v)]
    else:
        try:
            return [k, datetime.datetime.strptime(v, "%Y-%m-%dT%H:%M:%SZ").replace(tzinfo=datetime.timezone.utc)]
        except ValueError:
            try:
                return [k, datetime.datetime.strptime(v, "%Y-%m-%d").replace(tzinfo=datetime.timezone.utc)]
            except ValueError:
                return [k, v]

def main(name='', logy=False, *paths):
    """
    visualize sparse timeseries data in an easily readable way.

    usage:

    >> echo '
        timestamp=2021-10-07T00:38:00Z  a=160.2              b=20.8                c=1
        timestamp=2021-10-07T00:23:00Z  a=263.9333333333333  b=17.066666666666666  c=2
        timestamp=2021-10-07T00:08:00Z  a=360.4                                    c=4
        timestamp=2021-10-06T23:53:00Z  a=462.2              b=18.8                c=8
        timestamp=2021-10-06T23:38:00Z  a=562.1333333333333  b=18.866666666666667  c=16
        timestamp=2021-10-06T23:23:00Z                       b=26.333333333333332  c=32
        timestamp=2021-10-06T23:08:00Z  a=356.2              b=24.8                c=64
        timestamp=2021-10-06T22:53:00Z  a=460.1333333333333  b=20.866666666666667  c=128
        timestamp=2021-10-06T22:38:00Z  a=564.1333333333333                        c=256
        timestamp=2021-10-06T22:23:00Z  a=659.5333333333333  b=21.466666666666665  c=512
    ' | matplot
    """
    solarized()
    if paths:
        streams = [(os.path.basename(path), open(path)) for path in paths]
    else:
        streams = [('stdin', sys.stdin)]
    dfs = []
    columns = set()
    for _, stream in streams:
        xss = []
        for line in stream:
            line = line.strip()
            if not line:
                continue
            columns |= {x.split('=')[0] for x in line.split()}
            xs = dict([to_num(x) for x in line.split()])
            xss.append(xs)
        columns = sorted(columns)
        columns = sorted(columns, key=lambda x: x != 'timestamp')
        df = pandas.DataFrame(xss, columns=columns)
        if len(df) and isinstance(df[columns[0]][0], pandas.Timestamp):
            df = df.set_index(columns[0])
        dfs.append(df)
    df = dfs[0]
    for extra in dfs[1:]:
        df = df.combine_first(extra)
    df.plot(title=name, logy=logy, color=line_color, style=line_style)
    pyplot.show()

if __name__ == '__main__':
    argh.dispatch_command(main)